A deep convolutional neural network (CNN) can be used to classify images based on items shown in the images. For a certain level of training, CNNs have a finite discriminatory capacity. In a typical dataset, orientation of items varies between images and the discriminatory capacity of the resulting CNNs may be spent on being able to recognize the class of an item without regard to orientation. Accordingly, CNNs have difficulty in recognizing subtle differences between similar types of items.